Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f2a79166048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f2a7908ac18>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [38]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learn_rate')
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [39]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 32x32x3
        alpha = 0.2
        
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 16x16x64

        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 8x8x128

        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [40]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        
        alpha = 0.2
        
        # First fully connected layer
        x1 = tf.layers.dense(z, 3*3*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 3, 3, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 3x3x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 2, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 6x6x256 now
                     
        x3 = tf.layers.conv2d_transpose(x2, 128, 3, strides=2, padding='valid')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding='valid')
        # 28x28xoutdim now
        
        out = tf.tanh(logits)
        
#         print(x1)
#         print(x2)
#         print(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [41]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [42]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [43]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [50]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    losses = []
    steps = 0
    _,image_width, image_height, image_channels = data_shape
    # Keep in mind that the learning_rate_pl is the dictionary key for the scalar learning_rate value
    input_real, input_z , learning_rate_pl = model_inputs(image_width,image_height,image_channels,z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss,learning_rate_pl, beta1)
    n_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                image_input = 2 * batch_images #Scale images from abs(0.5) to 1
                steps +=1
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: image_input, input_z: batch_z, learning_rate_pl: learning_rate})
                
                _ = sess.run(g_opt, feed_dict={input_real: image_input, input_z: batch_z, learning_rate_pl: learning_rate})
                
                if steps % 10 == 0:
                    n_images = 9
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = sess.run(d_loss, feed_dict={input_z: batch_z, input_real: image_input, learning_rate_pl: learning_rate })
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [56]:
batch_size = 50
z_dim = 100
learning_rate = 0.002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.6904... Generator Loss: 10.6313
Epoch 1/2... Discriminator Loss: 0.2735... Generator Loss: 2.6753
Epoch 1/2... Discriminator Loss: 1.4376... Generator Loss: 0.5022
Epoch 1/2... Discriminator Loss: 2.0942... Generator Loss: 0.2641
Epoch 1/2... Discriminator Loss: 1.5635... Generator Loss: 2.6267
Epoch 1/2... Discriminator Loss: 0.9856... Generator Loss: 1.4696
Epoch 1/2... Discriminator Loss: 1.2415... Generator Loss: 2.4441
Epoch 1/2... Discriminator Loss: 1.5414... Generator Loss: 3.0573
Epoch 1/2... Discriminator Loss: 2.0958... Generator Loss: 0.2491
Epoch 1/2... Discriminator Loss: 1.6393... Generator Loss: 2.6984
Epoch 1/2... Discriminator Loss: 1.5486... Generator Loss: 0.4561
Epoch 1/2... Discriminator Loss: 1.0721... Generator Loss: 0.9572
Epoch 1/2... Discriminator Loss: 1.0103... Generator Loss: 0.7501
Epoch 1/2... Discriminator Loss: 1.2963... Generator Loss: 1.0509
Epoch 1/2... Discriminator Loss: 1.0750... Generator Loss: 0.8146
Epoch 1/2... Discriminator Loss: 1.5836... Generator Loss: 1.9521
Epoch 1/2... Discriminator Loss: 1.5892... Generator Loss: 2.8324
Epoch 1/2... Discriminator Loss: 1.4933... Generator Loss: 2.1733
Epoch 1/2... Discriminator Loss: 1.4001... Generator Loss: 0.5031
Epoch 1/2... Discriminator Loss: 1.4471... Generator Loss: 1.6279
Epoch 1/2... Discriminator Loss: 1.4669... Generator Loss: 0.4861
Epoch 1/2... Discriminator Loss: 0.6888... Generator Loss: 1.1407
Epoch 1/2... Discriminator Loss: 1.1692... Generator Loss: 2.3299
Epoch 1/2... Discriminator Loss: 1.2934... Generator Loss: 0.6978
Epoch 1/2... Discriminator Loss: 1.3081... Generator Loss: 0.6619
Epoch 1/2... Discriminator Loss: 0.8179... Generator Loss: 1.3668
Epoch 1/2... Discriminator Loss: 1.6345... Generator Loss: 0.3007
Epoch 1/2... Discriminator Loss: 1.1911... Generator Loss: 0.7069
Epoch 1/2... Discriminator Loss: 0.9659... Generator Loss: 0.9885
Epoch 1/2... Discriminator Loss: 1.7603... Generator Loss: 0.2524
Epoch 1/2... Discriminator Loss: 0.9933... Generator Loss: 2.4200
Epoch 1/2... Discriminator Loss: 1.0290... Generator Loss: 1.0371
Epoch 1/2... Discriminator Loss: 0.8656... Generator Loss: 1.4220
Epoch 1/2... Discriminator Loss: 0.9461... Generator Loss: 0.9164
Epoch 1/2... Discriminator Loss: 1.4322... Generator Loss: 0.4229
Epoch 1/2... Discriminator Loss: 1.0012... Generator Loss: 1.0363
Epoch 1/2... Discriminator Loss: 0.8364... Generator Loss: 1.1742
Epoch 1/2... Discriminator Loss: 0.9842... Generator Loss: 2.1428
Epoch 1/2... Discriminator Loss: 0.8913... Generator Loss: 1.5409
Epoch 1/2... Discriminator Loss: 0.9846... Generator Loss: 0.8207
Epoch 1/2... Discriminator Loss: 1.3214... Generator Loss: 0.4685
Epoch 1/2... Discriminator Loss: 1.3360... Generator Loss: 0.4823
Epoch 1/2... Discriminator Loss: 1.6579... Generator Loss: 2.1538
Epoch 1/2... Discriminator Loss: 1.8942... Generator Loss: 0.2481
Epoch 1/2... Discriminator Loss: 0.9425... Generator Loss: 1.4880
Epoch 1/2... Discriminator Loss: 2.4714... Generator Loss: 3.2382
Epoch 1/2... Discriminator Loss: 1.1274... Generator Loss: 0.7009
Epoch 1/2... Discriminator Loss: 2.1945... Generator Loss: 0.1845
Epoch 1/2... Discriminator Loss: 1.3942... Generator Loss: 0.4636
Epoch 1/2... Discriminator Loss: 1.1893... Generator Loss: 1.5754
Epoch 1/2... Discriminator Loss: 1.3846... Generator Loss: 0.4109
Epoch 1/2... Discriminator Loss: 1.7265... Generator Loss: 0.2997
Epoch 1/2... Discriminator Loss: 1.7229... Generator Loss: 0.3459
Epoch 1/2... Discriminator Loss: 1.0460... Generator Loss: 0.7965
Epoch 1/2... Discriminator Loss: 1.0724... Generator Loss: 0.7500
Epoch 1/2... Discriminator Loss: 1.0771... Generator Loss: 0.9172
Epoch 1/2... Discriminator Loss: 1.4401... Generator Loss: 0.4386
Epoch 1/2... Discriminator Loss: 1.0088... Generator Loss: 0.7298
Epoch 1/2... Discriminator Loss: 1.1393... Generator Loss: 0.7659
Epoch 1/2... Discriminator Loss: 1.5962... Generator Loss: 0.3360
Epoch 1/2... Discriminator Loss: 1.3408... Generator Loss: 0.4444
Epoch 1/2... Discriminator Loss: 0.9207... Generator Loss: 0.9761
Epoch 1/2... Discriminator Loss: 1.2521... Generator Loss: 0.6644
Epoch 1/2... Discriminator Loss: 1.0406... Generator Loss: 0.9588
Epoch 1/2... Discriminator Loss: 1.1796... Generator Loss: 0.6364
Epoch 1/2... Discriminator Loss: 1.4818... Generator Loss: 0.3448
Epoch 1/2... Discriminator Loss: 1.1284... Generator Loss: 0.6526
Epoch 1/2... Discriminator Loss: 1.0233... Generator Loss: 1.5058
Epoch 1/2... Discriminator Loss: 1.0064... Generator Loss: 0.8484
Epoch 1/2... Discriminator Loss: 1.1714... Generator Loss: 0.7587
Epoch 1/2... Discriminator Loss: 1.4961... Generator Loss: 2.2152
Epoch 1/2... Discriminator Loss: 1.0886... Generator Loss: 0.6503
Epoch 1/2... Discriminator Loss: 1.0189... Generator Loss: 0.7092
Epoch 1/2... Discriminator Loss: 0.7489... Generator Loss: 1.1397
Epoch 1/2... Discriminator Loss: 0.9947... Generator Loss: 0.8347
Epoch 1/2... Discriminator Loss: 1.3521... Generator Loss: 0.5068
Epoch 1/2... Discriminator Loss: 1.2559... Generator Loss: 0.5272
Epoch 1/2... Discriminator Loss: 1.1652... Generator Loss: 2.7630
Epoch 1/2... Discriminator Loss: 2.0903... Generator Loss: 0.2394
Epoch 1/2... Discriminator Loss: 1.1966... Generator Loss: 1.2373
Epoch 1/2... Discriminator Loss: 1.0359... Generator Loss: 1.0223
Epoch 1/2... Discriminator Loss: 1.0019... Generator Loss: 2.0609
Epoch 1/2... Discriminator Loss: 0.7770... Generator Loss: 1.5659
Epoch 1/2... Discriminator Loss: 1.0939... Generator Loss: 0.6085
Epoch 1/2... Discriminator Loss: 0.7750... Generator Loss: 1.4422
Epoch 1/2... Discriminator Loss: 1.2292... Generator Loss: 0.6298
Epoch 1/2... Discriminator Loss: 1.4302... Generator Loss: 0.4143
Epoch 1/2... Discriminator Loss: 1.4287... Generator Loss: 0.3753
Epoch 1/2... Discriminator Loss: 0.8790... Generator Loss: 0.8684
Epoch 1/2... Discriminator Loss: 1.3750... Generator Loss: 0.4461
Epoch 1/2... Discriminator Loss: 1.7145... Generator Loss: 0.2873
Epoch 1/2... Discriminator Loss: 0.8405... Generator Loss: 1.8494
Epoch 1/2... Discriminator Loss: 0.7508... Generator Loss: 1.0526
Epoch 1/2... Discriminator Loss: 0.8289... Generator Loss: 2.0213
Epoch 1/2... Discriminator Loss: 1.2021... Generator Loss: 1.0074
Epoch 1/2... Discriminator Loss: 1.4877... Generator Loss: 0.4394
Epoch 1/2... Discriminator Loss: 0.9792... Generator Loss: 1.0158
Epoch 1/2... Discriminator Loss: 0.8977... Generator Loss: 1.8145
Epoch 1/2... Discriminator Loss: 0.9899... Generator Loss: 0.8155
Epoch 1/2... Discriminator Loss: 1.5055... Generator Loss: 0.3990
Epoch 1/2... Discriminator Loss: 0.7938... Generator Loss: 1.2466
Epoch 1/2... Discriminator Loss: 1.6738... Generator Loss: 0.4441
Epoch 1/2... Discriminator Loss: 0.8891... Generator Loss: 1.7714
Epoch 1/2... Discriminator Loss: 1.1426... Generator Loss: 0.6243
Epoch 1/2... Discriminator Loss: 0.9919... Generator Loss: 0.8667
Epoch 1/2... Discriminator Loss: 1.4747... Generator Loss: 1.9596
Epoch 1/2... Discriminator Loss: 1.3133... Generator Loss: 0.4986
Epoch 1/2... Discriminator Loss: 1.1628... Generator Loss: 0.6069
Epoch 1/2... Discriminator Loss: 1.3860... Generator Loss: 0.5031
Epoch 1/2... Discriminator Loss: 0.8712... Generator Loss: 1.0006
Epoch 1/2... Discriminator Loss: 1.0501... Generator Loss: 1.1000
Epoch 1/2... Discriminator Loss: 0.7711... Generator Loss: 1.6857
Epoch 1/2... Discriminator Loss: 1.4002... Generator Loss: 0.4171
Epoch 1/2... Discriminator Loss: 1.6648... Generator Loss: 0.3295
Epoch 1/2... Discriminator Loss: 1.7968... Generator Loss: 0.3194
Epoch 1/2... Discriminator Loss: 1.4155... Generator Loss: 0.4794
Epoch 1/2... Discriminator Loss: 0.9436... Generator Loss: 1.8893
Epoch 1/2... Discriminator Loss: 1.3106... Generator Loss: 0.5176
Epoch 1/2... Discriminator Loss: 2.5574... Generator Loss: 0.1775
Epoch 1/2... Discriminator Loss: 0.8753... Generator Loss: 0.9191
Epoch 2/2... Discriminator Loss: 1.4143... Generator Loss: 0.4670
Epoch 2/2... Discriminator Loss: 1.3248... Generator Loss: 0.5254
Epoch 2/2... Discriminator Loss: 1.1517... Generator Loss: 0.6197
Epoch 2/2... Discriminator Loss: 0.7938... Generator Loss: 0.9443
Epoch 2/2... Discriminator Loss: 1.5757... Generator Loss: 2.7427
Epoch 2/2... Discriminator Loss: 0.8954... Generator Loss: 1.5244
Epoch 2/2... Discriminator Loss: 0.7347... Generator Loss: 1.1889
Epoch 2/2... Discriminator Loss: 1.5094... Generator Loss: 0.4193
Epoch 2/2... Discriminator Loss: 0.9669... Generator Loss: 1.3826
Epoch 2/2... Discriminator Loss: 0.8762... Generator Loss: 1.0145
Epoch 2/2... Discriminator Loss: 1.0294... Generator Loss: 0.6775
Epoch 2/2... Discriminator Loss: 0.6500... Generator Loss: 1.2497
Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 1.0292
Epoch 2/2... Discriminator Loss: 0.8222... Generator Loss: 1.5763
Epoch 2/2... Discriminator Loss: 0.9837... Generator Loss: 2.0694
Epoch 2/2... Discriminator Loss: 1.7976... Generator Loss: 0.2779
Epoch 2/2... Discriminator Loss: 0.7618... Generator Loss: 1.0608
Epoch 2/2... Discriminator Loss: 0.9507... Generator Loss: 1.2136
Epoch 2/2... Discriminator Loss: 0.9976... Generator Loss: 1.8266
Epoch 2/2... Discriminator Loss: 0.9209... Generator Loss: 0.9098
Epoch 2/2... Discriminator Loss: 0.6767... Generator Loss: 1.2461
Epoch 2/2... Discriminator Loss: 1.2615... Generator Loss: 0.5120
Epoch 2/2... Discriminator Loss: 1.5032... Generator Loss: 0.4479
Epoch 2/2... Discriminator Loss: 0.6273... Generator Loss: 1.2604
Epoch 2/2... Discriminator Loss: 1.6388... Generator Loss: 0.3998
Epoch 2/2... Discriminator Loss: 0.9013... Generator Loss: 1.2028
Epoch 2/2... Discriminator Loss: 1.2113... Generator Loss: 0.6551
Epoch 2/2... Discriminator Loss: 1.0637... Generator Loss: 1.1650
Epoch 2/2... Discriminator Loss: 0.9162... Generator Loss: 0.9418
Epoch 2/2... Discriminator Loss: 1.0531... Generator Loss: 0.8705
Epoch 2/2... Discriminator Loss: 0.8455... Generator Loss: 1.1402
Epoch 2/2... Discriminator Loss: 0.7232... Generator Loss: 1.7769
Epoch 2/2... Discriminator Loss: 0.6700... Generator Loss: 1.9057
Epoch 2/2... Discriminator Loss: 1.1064... Generator Loss: 1.5569
Epoch 2/2... Discriminator Loss: 0.9184... Generator Loss: 1.4066
Epoch 2/2... Discriminator Loss: 0.8749... Generator Loss: 1.1869
Epoch 2/2... Discriminator Loss: 0.8192... Generator Loss: 1.6354
Epoch 2/2... Discriminator Loss: 0.6631... Generator Loss: 1.2915
Epoch 2/2... Discriminator Loss: 0.8595... Generator Loss: 2.2900
Epoch 2/2... Discriminator Loss: 1.2415... Generator Loss: 0.5538
Epoch 2/2... Discriminator Loss: 1.0159... Generator Loss: 0.8480
Epoch 2/2... Discriminator Loss: 1.9509... Generator Loss: 0.2362
Epoch 2/2... Discriminator Loss: 0.9933... Generator Loss: 0.7460
Epoch 2/2... Discriminator Loss: 1.9736... Generator Loss: 0.3938
Epoch 2/2... Discriminator Loss: 0.8255... Generator Loss: 1.0537
Epoch 2/2... Discriminator Loss: 2.8268... Generator Loss: 0.1150
Epoch 2/2... Discriminator Loss: 1.1019... Generator Loss: 0.7621
Epoch 2/2... Discriminator Loss: 2.1755... Generator Loss: 0.2532
Epoch 2/2... Discriminator Loss: 1.2446... Generator Loss: 2.1684
Epoch 2/2... Discriminator Loss: 1.0716... Generator Loss: 0.6984
Epoch 2/2... Discriminator Loss: 1.1225... Generator Loss: 0.6989
Epoch 2/2... Discriminator Loss: 0.8463... Generator Loss: 1.0210
Epoch 2/2... Discriminator Loss: 0.9531... Generator Loss: 0.7621
Epoch 2/2... Discriminator Loss: 1.2752... Generator Loss: 0.5928
Epoch 2/2... Discriminator Loss: 0.7805... Generator Loss: 1.3316
Epoch 2/2... Discriminator Loss: 0.9412... Generator Loss: 1.7720
Epoch 2/2... Discriminator Loss: 0.8073... Generator Loss: 1.1294
Epoch 2/2... Discriminator Loss: 1.0895... Generator Loss: 1.7212
Epoch 2/2... Discriminator Loss: 1.2943... Generator Loss: 0.8506
Epoch 2/2... Discriminator Loss: 1.3965... Generator Loss: 0.4868
Epoch 2/2... Discriminator Loss: 1.0030... Generator Loss: 0.8671
Epoch 2/2... Discriminator Loss: 0.8432... Generator Loss: 1.8890
Epoch 2/2... Discriminator Loss: 0.8287... Generator Loss: 1.4858
Epoch 2/2... Discriminator Loss: 1.2457... Generator Loss: 0.4788
Epoch 2/2... Discriminator Loss: 1.0269... Generator Loss: 0.7854
Epoch 2/2... Discriminator Loss: 0.8746... Generator Loss: 1.7551
Epoch 2/2... Discriminator Loss: 1.1583... Generator Loss: 0.6370
Epoch 2/2... Discriminator Loss: 0.8496... Generator Loss: 0.9249
Epoch 2/2... Discriminator Loss: 0.7440... Generator Loss: 1.1467
Epoch 2/2... Discriminator Loss: 1.1068... Generator Loss: 0.9086
Epoch 2/2... Discriminator Loss: 1.7514... Generator Loss: 3.1201
Epoch 2/2... Discriminator Loss: 0.7380... Generator Loss: 1.3417
Epoch 2/2... Discriminator Loss: 1.4278... Generator Loss: 0.4743
Epoch 2/2... Discriminator Loss: 1.0162... Generator Loss: 0.7208
Epoch 2/2... Discriminator Loss: 0.5363... Generator Loss: 1.7764
Epoch 2/2... Discriminator Loss: 1.7007... Generator Loss: 0.4217
Epoch 2/2... Discriminator Loss: 2.4008... Generator Loss: 0.1789
Epoch 2/2... Discriminator Loss: 1.6362... Generator Loss: 0.3565
Epoch 2/2... Discriminator Loss: 1.6284... Generator Loss: 0.4424
Epoch 2/2... Discriminator Loss: 0.8105... Generator Loss: 1.1083
Epoch 2/2... Discriminator Loss: 0.9905... Generator Loss: 2.3734
Epoch 2/2... Discriminator Loss: 0.7471... Generator Loss: 2.1043
Epoch 2/2... Discriminator Loss: 0.8163... Generator Loss: 1.7817
Epoch 2/2... Discriminator Loss: 0.9946... Generator Loss: 0.7604
Epoch 2/2... Discriminator Loss: 1.2367... Generator Loss: 0.5135
Epoch 2/2... Discriminator Loss: 0.8102... Generator Loss: 2.1389
Epoch 2/2... Discriminator Loss: 0.8438... Generator Loss: 1.0346
Epoch 2/2... Discriminator Loss: 1.2422... Generator Loss: 0.8985
Epoch 2/2... Discriminator Loss: 0.8310... Generator Loss: 1.0111
Epoch 2/2... Discriminator Loss: 1.2359... Generator Loss: 1.3441
Epoch 2/2... Discriminator Loss: 1.3615... Generator Loss: 0.4861
Epoch 2/2... Discriminator Loss: 0.7704... Generator Loss: 1.1919
Epoch 2/2... Discriminator Loss: 0.8665... Generator Loss: 0.8871
Epoch 2/2... Discriminator Loss: 0.7802... Generator Loss: 1.0475
Epoch 2/2... Discriminator Loss: 1.0514... Generator Loss: 2.4318
Epoch 2/2... Discriminator Loss: 1.0928... Generator Loss: 0.8137
Epoch 2/2... Discriminator Loss: 1.5344... Generator Loss: 0.4121
Epoch 2/2... Discriminator Loss: 1.4154... Generator Loss: 0.4524
Epoch 2/2... Discriminator Loss: 1.0593... Generator Loss: 0.6674
Epoch 2/2... Discriminator Loss: 1.1248... Generator Loss: 0.7298
Epoch 2/2... Discriminator Loss: 1.8783... Generator Loss: 0.3923
Epoch 2/2... Discriminator Loss: 1.2875... Generator Loss: 0.5654
Epoch 2/2... Discriminator Loss: 0.9385... Generator Loss: 2.8952
Epoch 2/2... Discriminator Loss: 0.7857... Generator Loss: 0.9402
Epoch 2/2... Discriminator Loss: 1.8767... Generator Loss: 0.3097
Epoch 2/2... Discriminator Loss: 1.0156... Generator Loss: 0.7246
Epoch 2/2... Discriminator Loss: 0.7984... Generator Loss: 0.9634
Epoch 2/2... Discriminator Loss: 0.7243... Generator Loss: 1.0519
Epoch 2/2... Discriminator Loss: 1.1824... Generator Loss: 0.6613
Epoch 2/2... Discriminator Loss: 1.0297... Generator Loss: 0.8153
Epoch 2/2... Discriminator Loss: 1.1270... Generator Loss: 0.6653
Epoch 2/2... Discriminator Loss: 0.7974... Generator Loss: 2.0893
Epoch 2/2... Discriminator Loss: 2.7645... Generator Loss: 0.1518
Epoch 2/2... Discriminator Loss: 1.4314... Generator Loss: 3.2516
Epoch 2/2... Discriminator Loss: 0.7515... Generator Loss: 1.5267
Epoch 2/2... Discriminator Loss: 1.0215... Generator Loss: 1.5955
Epoch 2/2... Discriminator Loss: 1.0459... Generator Loss: 0.8270
Epoch 2/2... Discriminator Loss: 1.0675... Generator Loss: 0.7320
Epoch 2/2... Discriminator Loss: 1.4914... Generator Loss: 0.4147
Epoch 2/2... Discriminator Loss: 1.7975... Generator Loss: 0.4270

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [57]:
batch_size = 50
z_dim = 128
learning_rate = 0.002
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 12.6475... Generator Loss: 0.1231
Epoch 1/1... Discriminator Loss: 1.8630... Generator Loss: 0.3563
Epoch 1/1... Discriminator Loss: 1.7715... Generator Loss: 2.2784
Epoch 1/1... Discriminator Loss: 2.3899... Generator Loss: 0.1172
Epoch 1/1... Discriminator Loss: 1.3500... Generator Loss: 1.3520
Epoch 1/1... Discriminator Loss: 1.1447... Generator Loss: 1.6526
Epoch 1/1... Discriminator Loss: 1.3568... Generator Loss: 1.4617
Epoch 1/1... Discriminator Loss: 2.3949... Generator Loss: 0.1383
Epoch 1/1... Discriminator Loss: 1.5838... Generator Loss: 0.7755
Epoch 1/1... Discriminator Loss: 1.6022... Generator Loss: 0.4208
Epoch 1/1... Discriminator Loss: 1.6438... Generator Loss: 0.9199
Epoch 1/1... Discriminator Loss: 1.8084... Generator Loss: 0.3697
Epoch 1/1... Discriminator Loss: 1.4674... Generator Loss: 0.4303
Epoch 1/1... Discriminator Loss: 1.4493... Generator Loss: 0.9213
Epoch 1/1... Discriminator Loss: 1.3180... Generator Loss: 0.6475
Epoch 1/1... Discriminator Loss: 2.2631... Generator Loss: 1.5487
Epoch 1/1... Discriminator Loss: 1.4692... Generator Loss: 0.6246
Epoch 1/1... Discriminator Loss: 1.5726... Generator Loss: 1.4334
Epoch 1/1... Discriminator Loss: 1.5468... Generator Loss: 0.7917
Epoch 1/1... Discriminator Loss: 1.4571... Generator Loss: 0.5324
Epoch 1/1... Discriminator Loss: 1.4850... Generator Loss: 0.4418
Epoch 1/1... Discriminator Loss: 1.8023... Generator Loss: 1.3163
Epoch 1/1... Discriminator Loss: 1.3710... Generator Loss: 0.5713
Epoch 1/1... Discriminator Loss: 2.4611... Generator Loss: 0.1168
Epoch 1/1... Discriminator Loss: 1.4354... Generator Loss: 0.7987
Epoch 1/1... Discriminator Loss: 1.3780... Generator Loss: 0.8165
Epoch 1/1... Discriminator Loss: 1.3414... Generator Loss: 0.9594
Epoch 1/1... Discriminator Loss: 1.2161... Generator Loss: 0.8340
Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.9440
Epoch 1/1... Discriminator Loss: 1.7746... Generator Loss: 0.3464
Epoch 1/1... Discriminator Loss: 1.5341... Generator Loss: 0.7327
Epoch 1/1... Discriminator Loss: 1.6487... Generator Loss: 0.6328
Epoch 1/1... Discriminator Loss: 1.3892... Generator Loss: 0.5519
Epoch 1/1... Discriminator Loss: 1.3913... Generator Loss: 0.7679
Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.8625
Epoch 1/1... Discriminator Loss: 1.5107... Generator Loss: 0.7025
Epoch 1/1... Discriminator Loss: 1.4012... Generator Loss: 0.5461
Epoch 1/1... Discriminator Loss: 1.2889... Generator Loss: 0.8784
Epoch 1/1... Discriminator Loss: 1.5154... Generator Loss: 0.5202
Epoch 1/1... Discriminator Loss: 1.5523... Generator Loss: 0.9783
Epoch 1/1... Discriminator Loss: 1.6331... Generator Loss: 1.4657
Epoch 1/1... Discriminator Loss: 1.5445... Generator Loss: 0.4086
Epoch 1/1... Discriminator Loss: 1.6957... Generator Loss: 0.3330
Epoch 1/1... Discriminator Loss: 1.3289... Generator Loss: 0.6659
Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 0.8412
Epoch 1/1... Discriminator Loss: 1.4650... Generator Loss: 0.9842
Epoch 1/1... Discriminator Loss: 1.3900... Generator Loss: 0.4597
Epoch 1/1... Discriminator Loss: 1.5982... Generator Loss: 0.3971
Epoch 1/1... Discriminator Loss: 1.5009... Generator Loss: 0.3406
Epoch 1/1... Discriminator Loss: 1.4399... Generator Loss: 0.4498
Epoch 1/1... Discriminator Loss: 1.4474... Generator Loss: 0.4788
Epoch 1/1... Discriminator Loss: 1.2063... Generator Loss: 0.6839
Epoch 1/1... Discriminator Loss: 1.2937... Generator Loss: 0.8147
Epoch 1/1... Discriminator Loss: 1.3147... Generator Loss: 1.0855
Epoch 1/1... Discriminator Loss: 1.3129... Generator Loss: 0.8363
Epoch 1/1... Discriminator Loss: 1.3825... Generator Loss: 0.7370
Epoch 1/1... Discriminator Loss: 1.7157... Generator Loss: 0.3519
Epoch 1/1... Discriminator Loss: 1.4378... Generator Loss: 0.6472
Epoch 1/1... Discriminator Loss: 1.3685... Generator Loss: 0.7952
Epoch 1/1... Discriminator Loss: 1.4955... Generator Loss: 1.1318
Epoch 1/1... Discriminator Loss: 1.4122... Generator Loss: 0.9977
Epoch 1/1... Discriminator Loss: 1.2365... Generator Loss: 0.5816
Epoch 1/1... Discriminator Loss: 1.3665... Generator Loss: 0.6772
Epoch 1/1... Discriminator Loss: 1.4399... Generator Loss: 0.5214
Epoch 1/1... Discriminator Loss: 1.5366... Generator Loss: 1.0919
Epoch 1/1... Discriminator Loss: 1.4961... Generator Loss: 0.5531
Epoch 1/1... Discriminator Loss: 1.3647... Generator Loss: 0.5328
Epoch 1/1... Discriminator Loss: 1.5270... Generator Loss: 1.1405
Epoch 1/1... Discriminator Loss: 1.4983... Generator Loss: 0.4989
Epoch 1/1... Discriminator Loss: 1.4409... Generator Loss: 0.6533
Epoch 1/1... Discriminator Loss: 1.3597... Generator Loss: 0.6036
Epoch 1/1... Discriminator Loss: 1.4773... Generator Loss: 0.6607
Epoch 1/1... Discriminator Loss: 1.5177... Generator Loss: 0.8216
Epoch 1/1... Discriminator Loss: 1.4552... Generator Loss: 0.6149
Epoch 1/1... Discriminator Loss: 1.3314... Generator Loss: 0.6211
Epoch 1/1... Discriminator Loss: 1.4317... Generator Loss: 0.5958
Epoch 1/1... Discriminator Loss: 1.3588... Generator Loss: 0.7247
Epoch 1/1... Discriminator Loss: 1.3796... Generator Loss: 0.6392
Epoch 1/1... Discriminator Loss: 1.5665... Generator Loss: 0.4057
Epoch 1/1... Discriminator Loss: 1.5512... Generator Loss: 0.3705
Epoch 1/1... Discriminator Loss: 2.0509... Generator Loss: 1.7005
Epoch 1/1... Discriminator Loss: 1.0237... Generator Loss: 0.7695
Epoch 1/1... Discriminator Loss: 1.4101... Generator Loss: 0.7641
Epoch 1/1... Discriminator Loss: 1.3350... Generator Loss: 0.7165
Epoch 1/1... Discriminator Loss: 1.4378... Generator Loss: 0.7340
Epoch 1/1... Discriminator Loss: 1.6475... Generator Loss: 0.3062
Epoch 1/1... Discriminator Loss: 1.3490... Generator Loss: 0.7137
Epoch 1/1... Discriminator Loss: 1.5972... Generator Loss: 1.3505
Epoch 1/1... Discriminator Loss: 1.2914... Generator Loss: 0.5721
Epoch 1/1... Discriminator Loss: 1.6153... Generator Loss: 1.2080
Epoch 1/1... Discriminator Loss: 1.3234... Generator Loss: 0.7806
Epoch 1/1... Discriminator Loss: 1.2896... Generator Loss: 0.8151
Epoch 1/1... Discriminator Loss: 1.1346... Generator Loss: 0.8132
Epoch 1/1... Discriminator Loss: 1.4181... Generator Loss: 0.5021
Epoch 1/1... Discriminator Loss: 1.3760... Generator Loss: 0.6511
Epoch 1/1... Discriminator Loss: 1.4585... Generator Loss: 0.4395
Epoch 1/1... Discriminator Loss: 1.2830... Generator Loss: 0.6524
Epoch 1/1... Discriminator Loss: 1.6575... Generator Loss: 0.3419
Epoch 1/1... Discriminator Loss: 1.4992... Generator Loss: 0.4631
Epoch 1/1... Discriminator Loss: 1.2485... Generator Loss: 0.5797
Epoch 1/1... Discriminator Loss: 1.3629... Generator Loss: 0.9064
Epoch 1/1... Discriminator Loss: 1.3876... Generator Loss: 0.5201
Epoch 1/1... Discriminator Loss: 1.8051... Generator Loss: 0.2906
Epoch 1/1... Discriminator Loss: 1.3909... Generator Loss: 0.6491
Epoch 1/1... Discriminator Loss: 1.4016... Generator Loss: 1.1153
Epoch 1/1... Discriminator Loss: 1.3395... Generator Loss: 0.7119
Epoch 1/1... Discriminator Loss: 1.3579... Generator Loss: 0.5829
Epoch 1/1... Discriminator Loss: 1.2029... Generator Loss: 0.6755
Epoch 1/1... Discriminator Loss: 1.2506... Generator Loss: 0.6967
Epoch 1/1... Discriminator Loss: 1.1855... Generator Loss: 0.7077
Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.5717
Epoch 1/1... Discriminator Loss: 1.4216... Generator Loss: 1.1415
Epoch 1/1... Discriminator Loss: 1.3387... Generator Loss: 0.4773
Epoch 1/1... Discriminator Loss: 1.5168... Generator Loss: 0.4919
Epoch 1/1... Discriminator Loss: 1.2490... Generator Loss: 0.5944
Epoch 1/1... Discriminator Loss: 1.5874... Generator Loss: 0.3878
Epoch 1/1... Discriminator Loss: 1.5007... Generator Loss: 0.8383
Epoch 1/1... Discriminator Loss: 1.1398... Generator Loss: 0.6147
Epoch 1/1... Discriminator Loss: 1.3162... Generator Loss: 0.5765
Epoch 1/1... Discriminator Loss: 1.2966... Generator Loss: 0.7242
Epoch 1/1... Discriminator Loss: 1.3978... Generator Loss: 0.7151
Epoch 1/1... Discriminator Loss: 1.2763... Generator Loss: 0.6455
Epoch 1/1... Discriminator Loss: 1.3314... Generator Loss: 0.9702
Epoch 1/1... Discriminator Loss: 1.1998... Generator Loss: 0.6752
Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.6747
Epoch 1/1... Discriminator Loss: 1.4865... Generator Loss: 0.3546
Epoch 1/1... Discriminator Loss: 1.3294... Generator Loss: 0.6883
Epoch 1/1... Discriminator Loss: 1.5154... Generator Loss: 0.3669
Epoch 1/1... Discriminator Loss: 1.3306... Generator Loss: 0.7272
Epoch 1/1... Discriminator Loss: 1.5156... Generator Loss: 0.5895
Epoch 1/1... Discriminator Loss: 1.3539... Generator Loss: 0.9765
Epoch 1/1... Discriminator Loss: 1.3825... Generator Loss: 0.6750
Epoch 1/1... Discriminator Loss: 1.3430... Generator Loss: 0.5184
Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.6707
Epoch 1/1... Discriminator Loss: 1.3613... Generator Loss: 0.4832
Epoch 1/1... Discriminator Loss: 1.3647... Generator Loss: 1.0373
Epoch 1/1... Discriminator Loss: 1.2211... Generator Loss: 0.9101
Epoch 1/1... Discriminator Loss: 1.2819... Generator Loss: 0.6640
Epoch 1/1... Discriminator Loss: 1.2798... Generator Loss: 0.7274
Epoch 1/1... Discriminator Loss: 1.4357... Generator Loss: 0.6371
Epoch 1/1... Discriminator Loss: 1.2320... Generator Loss: 0.6891
Epoch 1/1... Discriminator Loss: 1.2044... Generator Loss: 0.8929
Epoch 1/1... Discriminator Loss: 1.3368... Generator Loss: 0.6096
Epoch 1/1... Discriminator Loss: 1.3370... Generator Loss: 0.5111
Epoch 1/1... Discriminator Loss: 1.3394... Generator Loss: 0.8347
Epoch 1/1... Discriminator Loss: 1.3258... Generator Loss: 0.6600
Epoch 1/1... Discriminator Loss: 1.3628... Generator Loss: 0.8113
Epoch 1/1... Discriminator Loss: 1.3552... Generator Loss: 0.5991
Epoch 1/1... Discriminator Loss: 1.2618... Generator Loss: 0.5553
Epoch 1/1... Discriminator Loss: 1.6689... Generator Loss: 0.3240
Epoch 1/1... Discriminator Loss: 1.2775... Generator Loss: 0.6147
Epoch 1/1... Discriminator Loss: 1.4296... Generator Loss: 0.9333
Epoch 1/1... Discriminator Loss: 1.4250... Generator Loss: 0.6244
Epoch 1/1... Discriminator Loss: 1.3090... Generator Loss: 0.6260
Epoch 1/1... Discriminator Loss: 1.1270... Generator Loss: 0.8509
Epoch 1/1... Discriminator Loss: 1.4434... Generator Loss: 0.4831
Epoch 1/1... Discriminator Loss: 1.2610... Generator Loss: 0.5912
Epoch 1/1... Discriminator Loss: 1.2944... Generator Loss: 0.6134
Epoch 1/1... Discriminator Loss: 1.3363... Generator Loss: 0.8384
Epoch 1/1... Discriminator Loss: 1.2788... Generator Loss: 0.5984
Epoch 1/1... Discriminator Loss: 1.2886... Generator Loss: 0.6871
Epoch 1/1... Discriminator Loss: 1.2584... Generator Loss: 0.7689
Epoch 1/1... Discriminator Loss: 1.2624... Generator Loss: 0.5934
Epoch 1/1... Discriminator Loss: 1.4146... Generator Loss: 0.7290
Epoch 1/1... Discriminator Loss: 1.2611... Generator Loss: 0.8244
Epoch 1/1... Discriminator Loss: 1.2639... Generator Loss: 0.7735
Epoch 1/1... Discriminator Loss: 1.2739... Generator Loss: 0.7614
Epoch 1/1... Discriminator Loss: 1.6837... Generator Loss: 0.6238
Epoch 1/1... Discriminator Loss: 1.2260... Generator Loss: 0.7896
Epoch 1/1... Discriminator Loss: 1.3309... Generator Loss: 0.5803
Epoch 1/1... Discriminator Loss: 1.5003... Generator Loss: 0.4048
Epoch 1/1... Discriminator Loss: 1.3423... Generator Loss: 0.6421
Epoch 1/1... Discriminator Loss: 1.3095... Generator Loss: 0.7661
Epoch 1/1... Discriminator Loss: 1.3452... Generator Loss: 0.4389
Epoch 1/1... Discriminator Loss: 1.2982... Generator Loss: 0.7977
Epoch 1/1... Discriminator Loss: 1.2283... Generator Loss: 0.6851
Epoch 1/1... Discriminator Loss: 1.3199... Generator Loss: 0.5435
Epoch 1/1... Discriminator Loss: 1.3037... Generator Loss: 0.5254
Epoch 1/1... Discriminator Loss: 1.2338... Generator Loss: 1.1662
Epoch 1/1... Discriminator Loss: 1.2843... Generator Loss: 0.8468
Epoch 1/1... Discriminator Loss: 1.1797... Generator Loss: 0.6333
Epoch 1/1... Discriminator Loss: 1.3629... Generator Loss: 0.4872
Epoch 1/1... Discriminator Loss: 1.2586... Generator Loss: 0.9393
Epoch 1/1... Discriminator Loss: 1.2119... Generator Loss: 0.7444
Epoch 1/1... Discriminator Loss: 1.3529... Generator Loss: 0.4725
Epoch 1/1... Discriminator Loss: 1.4000... Generator Loss: 0.9237
Epoch 1/1... Discriminator Loss: 1.3005... Generator Loss: 0.5648
Epoch 1/1... Discriminator Loss: 1.1961... Generator Loss: 0.8509
Epoch 1/1... Discriminator Loss: 1.3347... Generator Loss: 0.6428
Epoch 1/1... Discriminator Loss: 1.2708... Generator Loss: 0.5785
Epoch 1/1... Discriminator Loss: 1.3315... Generator Loss: 0.9954
Epoch 1/1... Discriminator Loss: 1.2751... Generator Loss: 0.6554
Epoch 1/1... Discriminator Loss: 1.2899... Generator Loss: 0.9718
Epoch 1/1... Discriminator Loss: 1.4315... Generator Loss: 1.1819
Epoch 1/1... Discriminator Loss: 1.2435... Generator Loss: 0.7191
Epoch 1/1... Discriminator Loss: 1.1349... Generator Loss: 0.7386
Epoch 1/1... Discriminator Loss: 1.0794... Generator Loss: 0.9222
Epoch 1/1... Discriminator Loss: 1.7457... Generator Loss: 1.3782
Epoch 1/1... Discriminator Loss: 1.3936... Generator Loss: 0.4354
Epoch 1/1... Discriminator Loss: 1.0792... Generator Loss: 0.7584
Epoch 1/1... Discriminator Loss: 1.2345... Generator Loss: 0.8001
Epoch 1/1... Discriminator Loss: 1.4111... Generator Loss: 0.7015
Epoch 1/1... Discriminator Loss: 1.2885... Generator Loss: 1.1498
Epoch 1/1... Discriminator Loss: 1.5967... Generator Loss: 0.4066
Epoch 1/1... Discriminator Loss: 1.2580... Generator Loss: 0.6628
Epoch 1/1... Discriminator Loss: 1.7267... Generator Loss: 1.5435
Epoch 1/1... Discriminator Loss: 1.3277... Generator Loss: 0.8514
Epoch 1/1... Discriminator Loss: 1.4752... Generator Loss: 0.5457
Epoch 1/1... Discriminator Loss: 1.2439... Generator Loss: 0.7748
Epoch 1/1... Discriminator Loss: 1.2704... Generator Loss: 0.8264
Epoch 1/1... Discriminator Loss: 1.3922... Generator Loss: 0.4966
Epoch 1/1... Discriminator Loss: 1.1780... Generator Loss: 0.7874
Epoch 1/1... Discriminator Loss: 1.4439... Generator Loss: 0.4886
Epoch 1/1... Discriminator Loss: 1.2351... Generator Loss: 0.8557
Epoch 1/1... Discriminator Loss: 1.2808... Generator Loss: 1.0590
Epoch 1/1... Discriminator Loss: 1.3179... Generator Loss: 0.6007
Epoch 1/1... Discriminator Loss: 1.2970... Generator Loss: 0.5011
Epoch 1/1... Discriminator Loss: 1.3280... Generator Loss: 1.1949
Epoch 1/1... Discriminator Loss: 1.3284... Generator Loss: 1.0379
Epoch 1/1... Discriminator Loss: 1.2949... Generator Loss: 0.7204
Epoch 1/1... Discriminator Loss: 1.3414... Generator Loss: 0.5459
Epoch 1/1... Discriminator Loss: 1.7335... Generator Loss: 0.2961
Epoch 1/1... Discriminator Loss: 1.2847... Generator Loss: 0.6318
Epoch 1/1... Discriminator Loss: 1.4625... Generator Loss: 0.4066
Epoch 1/1... Discriminator Loss: 1.3560... Generator Loss: 0.5224
Epoch 1/1... Discriminator Loss: 1.4208... Generator Loss: 0.4600
Epoch 1/1... Discriminator Loss: 1.6183... Generator Loss: 1.4658
Epoch 1/1... Discriminator Loss: 1.3376... Generator Loss: 1.0918
Epoch 1/1... Discriminator Loss: 1.4871... Generator Loss: 0.4005
Epoch 1/1... Discriminator Loss: 1.3464... Generator Loss: 0.6354
Epoch 1/1... Discriminator Loss: 1.6213... Generator Loss: 0.2969
Epoch 1/1... Discriminator Loss: 1.1592... Generator Loss: 0.7161
Epoch 1/1... Discriminator Loss: 1.3222... Generator Loss: 0.5587
Epoch 1/1... Discriminator Loss: 1.5010... Generator Loss: 0.3876
Epoch 1/1... Discriminator Loss: 1.1725... Generator Loss: 0.8481
Epoch 1/1... Discriminator Loss: 1.2907... Generator Loss: 0.6300
Epoch 1/1... Discriminator Loss: 1.2800... Generator Loss: 0.6031
Epoch 1/1... Discriminator Loss: 1.9072... Generator Loss: 0.2380
Epoch 1/1... Discriminator Loss: 1.2165... Generator Loss: 0.7128
Epoch 1/1... Discriminator Loss: 1.3504... Generator Loss: 0.8551
Epoch 1/1... Discriminator Loss: 1.4019... Generator Loss: 0.8578
Epoch 1/1... Discriminator Loss: 1.2333... Generator Loss: 0.6618
Epoch 1/1... Discriminator Loss: 1.2217... Generator Loss: 0.8454
Epoch 1/1... Discriminator Loss: 1.4094... Generator Loss: 0.5848
Epoch 1/1... Discriminator Loss: 1.4190... Generator Loss: 0.6267
Epoch 1/1... Discriminator Loss: 1.3813... Generator Loss: 0.6912
Epoch 1/1... Discriminator Loss: 1.2669... Generator Loss: 0.6230
Epoch 1/1... Discriminator Loss: 1.3784... Generator Loss: 1.2101
Epoch 1/1... Discriminator Loss: 1.2013... Generator Loss: 0.8889
Epoch 1/1... Discriminator Loss: 1.2070... Generator Loss: 0.5862
Epoch 1/1... Discriminator Loss: 1.4561... Generator Loss: 0.4487
Epoch 1/1... Discriminator Loss: 1.4973... Generator Loss: 1.1458
Epoch 1/1... Discriminator Loss: 1.3274... Generator Loss: 0.5166
Epoch 1/1... Discriminator Loss: 1.7018... Generator Loss: 0.2858
Epoch 1/1... Discriminator Loss: 1.2880... Generator Loss: 0.8447
Epoch 1/1... Discriminator Loss: 1.2742... Generator Loss: 0.7087
Epoch 1/1... Discriminator Loss: 1.2737... Generator Loss: 0.6260
Epoch 1/1... Discriminator Loss: 1.1906... Generator Loss: 0.9248
Epoch 1/1... Discriminator Loss: 1.3057... Generator Loss: 0.5137
Epoch 1/1... Discriminator Loss: 1.1822... Generator Loss: 0.9196
Epoch 1/1... Discriminator Loss: 1.2486... Generator Loss: 0.5518
Epoch 1/1... Discriminator Loss: 1.5538... Generator Loss: 0.3590
Epoch 1/1... Discriminator Loss: 1.3022... Generator Loss: 0.6410
Epoch 1/1... Discriminator Loss: 1.3036... Generator Loss: 0.7208
Epoch 1/1... Discriminator Loss: 1.1319... Generator Loss: 0.8940
Epoch 1/1... Discriminator Loss: 1.3773... Generator Loss: 0.8244
Epoch 1/1... Discriminator Loss: 1.2920... Generator Loss: 0.6431
Epoch 1/1... Discriminator Loss: 1.3134... Generator Loss: 0.6904
Epoch 1/1... Discriminator Loss: 1.3732... Generator Loss: 0.4511
Epoch 1/1... Discriminator Loss: 1.3869... Generator Loss: 1.0080
Epoch 1/1... Discriminator Loss: 1.2764... Generator Loss: 0.5101
Epoch 1/1... Discriminator Loss: 1.2630... Generator Loss: 0.4891
Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.5789
Epoch 1/1... Discriminator Loss: 1.4810... Generator Loss: 0.4010
Epoch 1/1... Discriminator Loss: 1.4298... Generator Loss: 0.6742
Epoch 1/1... Discriminator Loss: 1.3904... Generator Loss: 0.7099
Epoch 1/1... Discriminator Loss: 1.3592... Generator Loss: 0.8036
Epoch 1/1... Discriminator Loss: 1.3902... Generator Loss: 0.5549
Epoch 1/1... Discriminator Loss: 1.3025... Generator Loss: 0.9099
Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.4727
Epoch 1/1... Discriminator Loss: 1.3687... Generator Loss: 0.9565
Epoch 1/1... Discriminator Loss: 1.4183... Generator Loss: 0.4229
Epoch 1/1... Discriminator Loss: 1.3360... Generator Loss: 0.7776
Epoch 1/1... Discriminator Loss: 1.2118... Generator Loss: 0.6156
Epoch 1/1... Discriminator Loss: 1.8564... Generator Loss: 0.2290
Epoch 1/1... Discriminator Loss: 1.2265... Generator Loss: 0.6673
Epoch 1/1... Discriminator Loss: 1.7263... Generator Loss: 0.2839
Epoch 1/1... Discriminator Loss: 1.3166... Generator Loss: 0.4456
Epoch 1/1... Discriminator Loss: 1.1575... Generator Loss: 1.0558
Epoch 1/1... Discriminator Loss: 1.1654... Generator Loss: 0.9864
Epoch 1/1... Discriminator Loss: 1.4294... Generator Loss: 0.6142
Epoch 1/1... Discriminator Loss: 1.2930... Generator Loss: 1.0420
Epoch 1/1... Discriminator Loss: 1.5586... Generator Loss: 0.8775
Epoch 1/1... Discriminator Loss: 1.4782... Generator Loss: 0.4013
Epoch 1/1... Discriminator Loss: 1.2946... Generator Loss: 0.5507
Epoch 1/1... Discriminator Loss: 1.2246... Generator Loss: 0.6828
Epoch 1/1... Discriminator Loss: 1.3212... Generator Loss: 0.6736
Epoch 1/1... Discriminator Loss: 1.2522... Generator Loss: 0.8813
Epoch 1/1... Discriminator Loss: 1.3311... Generator Loss: 0.4776
Epoch 1/1... Discriminator Loss: 1.1961... Generator Loss: 0.6933
Epoch 1/1... Discriminator Loss: 1.6363... Generator Loss: 0.2872
Epoch 1/1... Discriminator Loss: 1.7733... Generator Loss: 0.2375
Epoch 1/1... Discriminator Loss: 1.3721... Generator Loss: 0.8189
Epoch 1/1... Discriminator Loss: 1.3851... Generator Loss: 0.4649
Epoch 1/1... Discriminator Loss: 1.2136... Generator Loss: 0.5695
Epoch 1/1... Discriminator Loss: 1.1376... Generator Loss: 0.7492
Epoch 1/1... Discriminator Loss: 1.2920... Generator Loss: 0.8424
Epoch 1/1... Discriminator Loss: 1.4912... Generator Loss: 0.4329
Epoch 1/1... Discriminator Loss: 1.5185... Generator Loss: 0.3539
Epoch 1/1... Discriminator Loss: 1.3303... Generator Loss: 0.5138
Epoch 1/1... Discriminator Loss: 1.1207... Generator Loss: 0.7909
Epoch 1/1... Discriminator Loss: 1.1688... Generator Loss: 0.8165
Epoch 1/1... Discriminator Loss: 1.2004... Generator Loss: 0.7961
Epoch 1/1... Discriminator Loss: 1.4593... Generator Loss: 0.5852
Epoch 1/1... Discriminator Loss: 1.2355... Generator Loss: 0.6364
Epoch 1/1... Discriminator Loss: 1.3159... Generator Loss: 0.6773
Epoch 1/1... Discriminator Loss: 1.5776... Generator Loss: 0.3742
Epoch 1/1... Discriminator Loss: 1.4593... Generator Loss: 0.3756
Epoch 1/1... Discriminator Loss: 1.2029... Generator Loss: 0.7070
Epoch 1/1... Discriminator Loss: 1.3156... Generator Loss: 0.6400
Epoch 1/1... Discriminator Loss: 1.3654... Generator Loss: 0.5996
Epoch 1/1... Discriminator Loss: 1.2778... Generator Loss: 0.4171
Epoch 1/1... Discriminator Loss: 1.2577... Generator Loss: 1.0566
Epoch 1/1... Discriminator Loss: 1.2759... Generator Loss: 0.8088
Epoch 1/1... Discriminator Loss: 1.1295... Generator Loss: 0.7491
Epoch 1/1... Discriminator Loss: 1.2911... Generator Loss: 0.5112
Epoch 1/1... Discriminator Loss: 1.5037... Generator Loss: 0.6477
Epoch 1/1... Discriminator Loss: 1.2158... Generator Loss: 0.7321
Epoch 1/1... Discriminator Loss: 1.3391... Generator Loss: 0.4868
Epoch 1/1... Discriminator Loss: 1.1030... Generator Loss: 1.1386
Epoch 1/1... Discriminator Loss: 1.1779... Generator Loss: 0.7631
Epoch 1/1... Discriminator Loss: 1.4530... Generator Loss: 0.5553
Epoch 1/1... Discriminator Loss: 1.3688... Generator Loss: 1.1746
Epoch 1/1... Discriminator Loss: 1.3206... Generator Loss: 0.6375
Epoch 1/1... Discriminator Loss: 1.1689... Generator Loss: 1.2088
Epoch 1/1... Discriminator Loss: 1.1752... Generator Loss: 0.8790
Epoch 1/1... Discriminator Loss: 1.3615... Generator Loss: 0.5801
Epoch 1/1... Discriminator Loss: 1.0979... Generator Loss: 0.7185
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-57-04894ee24c2e> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-50-f03156a1f6f8> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     48                           "Generator Loss: {:.4f}".format(train_loss_g))
     49 
---> 50                     show_generator_output(sess, n_images, input_z, image_channels, data_image_mode)
     51 

<ipython-input-43-73239a44c0dc> in show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode)
     19     samples = sess.run(
     20         generator(input_z, out_channel_dim, False),
---> 21         feed_dict={input_z: example_z})
     22 
     23     images_grid = helper.images_square_grid(samples, image_mode)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
    998                 run_metadata):
    999       # Ensure any changes to the graph are reflected in the runtime.
-> 1000       self._extend_graph()
   1001       with errors.raise_exception_on_not_ok_status() as status:
   1002         return tf_session.TF_Run(session, options,

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _extend_graph(self)
   1047         with errors.raise_exception_on_not_ok_status() as status:
   1048           tf_session.TF_ExtendGraph(
-> 1049               self._session, graph_def.SerializeToString(), status)
   1050         self._opened = True
   1051 

KeyboardInterrupt: 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.